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Notes on scientific machine learning
3 notes
CheMLFlow: An Open-Source Platform for Cheminformatics and Materials Informatics Applications
CheMLFlow is an open-source platform designed to help researchers build end-to-end, high-throughput, and agentic workflows for various scientific and technological applications. It aims to address the common challenge…
Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields
Researchers propose a new method to train Attention Graph Neural Networks for predicting three-dimensional thermo-fluid fields. Instead of using conventional numerical solvers that incur high computational costs, they…
Physics-Audited Agentic Discovery in Scientific Machine Learning
Researchers have developed Physics-Audited Agentic Scientific Machine Learning (PA-SciML), a new workflow for machine learning in science that ensures discovered models follow certain physics rules such as boundary…
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